






"""simple docstring"""





from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap







def 	lowerCamelCase__			(				_lowerCamelCase			:						Union[str, Any]					) ->							Optional[int]:
  return getitem, k







def 	lowerCamelCase__			(				_lowerCamelCase			:						List[str]						,							_lowerCamelCase			:						Optional[int]					) ->							int:
  return setitem, k, v







def 	lowerCamelCase__			(				_lowerCamelCase			:						str					) ->							Tuple:
  return delitem, k







def 	lowerCamelCase__			(				_lowerCamelCase			:						Any						,							_lowerCamelCase			:						Optional[Any]						,							*_lowerCamelCase			:						str					) ->							Union[str, Any]:
  try:
    return fun(_lowerCamelCase						,							*_lowerCamelCase					), None
  except Exception as e:
    return None, e


_SCREAMING_SNAKE_CASE   :							List[str]          =		(
    _set('''key_a''', '''val_a'''),
    _set('''key_b''', '''val_b'''),
)

_SCREAMING_SNAKE_CASE   :							Optional[Any]          =		[
    _set('''key_a''', '''val_a'''),
    _set('''key_a''', '''val_b'''),
]

_SCREAMING_SNAKE_CASE   :							Optional[int]          =		[
    _set('''key_a''', '''val_a'''),
    _set('''key_b''', '''val_b'''),
    _del('''key_a'''),
    _del('''key_b'''),
    _set('''key_a''', '''val_a'''),
    _del('''key_a'''),
]

_SCREAMING_SNAKE_CASE   :							Dict          =		[
    _get('''key_a'''),
    _del('''key_a'''),
    _set('''key_a''', '''val_a'''),
    _del('''key_a'''),
    _del('''key_a'''),
    _get('''key_a'''),
]

_SCREAMING_SNAKE_CASE   :							int          =		[
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

_SCREAMING_SNAKE_CASE   :							Any          =		[
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set('''key_a''', '''val_b'''),
]







@pytest.mark.parametrize(
    'operations'						,							(
        pytest.param(_add_items						,							id='add items'					),
        pytest.param(_overwrite_items						,							id='overwrite items'					),
        pytest.param(_delete_items						,							id='delete items'					),
        pytest.param(_access_absent_items						,							id='access absent items'					),
        pytest.param(_add_with_resize_up						,							id='add with resize up'					),
        pytest.param(_add_with_resize_down						,							id='add with resize down'					),
    )						,							)
def 	lowerCamelCase__			(				_lowerCamelCase			:						List[Any]					) ->							Dict:
  lowerCamelCase_       	=		HashMap(initial_block_size=4					)
  lowerCamelCase_       	=		{}
  for _, (fun, *args) in enumerate(_lowerCamelCase					):
    lowerCamelCase_	,      lowerCamelCase_       	=		_run_operation(_lowerCamelCase						,							_lowerCamelCase						,							*_lowerCamelCase					)
    lowerCamelCase_	,      lowerCamelCase_       	=		_run_operation(_lowerCamelCase						,							_lowerCamelCase						,							*_lowerCamelCase					)
    assert my_res == py_res
    assert str(_lowerCamelCase					) == str(_lowerCamelCase					)
    assert set(_lowerCamelCase					) == set(_lowerCamelCase					)
    assert len(_lowerCamelCase					) == len(_lowerCamelCase					)
    assert set(my.items()					) == set(py.items()					)







def 	lowerCamelCase__			(				) ->							Dict:
  def is_public(_lowerCamelCase			:						str					) -> bool:
    return not name.startswith('_'					)

  lowerCamelCase_       	=		{name for name in dir({}					) if is_public(_lowerCamelCase					)}
  lowerCamelCase_       	=		{name for name in dir(HashMap()					) if is_public(_lowerCamelCase					)}

  assert dict_public_names > hash_public_names












"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 	a		(   __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					KandinskyImgaImgPipeline
    SCREAMING_SNAKE_CASE		:	Tuple		=					["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
    SCREAMING_SNAKE_CASE		:	int		=					[
        """prompt""",
        """negative_prompt""",
        """image_embeds""",
        """negative_image_embeds""",
        """image""",
    ]
    SCREAMING_SNAKE_CASE		:	List[str]		=					[
        """generator""",
        """height""",
        """width""",
        """strength""",
        """guidance_scale""",
        """negative_prompt""",
        """num_inference_steps""",
        """return_dict""",
        """guidance_scale""",
        """num_images_per_prompt""",
        """output_type""",
        """return_dict""",
    ]
    SCREAMING_SNAKE_CASE		:	Any		=					False






    @property
    def   UpperCamelCase			(			self	:				List[str]   )	->						str:
      return 32






    @property
    def   UpperCamelCase			(			self	:				Tuple   )	->						Optional[Any]:
      return 32






    @property
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						str:
      return self.time_input_dim






    @property
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Optional[Any]:
      return self.time_input_dim * 4






    @property
    def   UpperCamelCase			(			self	:				int   )	->						List[Any]:
      return 100






    @property
    def   UpperCamelCase			(			self	:				Any   )	->						List[Any]:
      lowerCamelCase_       	=		XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'   )
      return tokenizer






    @property
    def   UpperCamelCase			(			self	:				str   )	->						Optional[Any]:
      torch.manual_seed(0   )
      lowerCamelCase_       	=		MCLIPConfig(
          numDims=self.cross_attention_dim			,		transformerDimensions=self.text_embedder_hidden_size			,		hidden_size=self.text_embedder_hidden_size			,		intermediate_size=37			,		num_attention_heads=4			,		num_hidden_layers=5			,		vocab_size=1005			,		)

      lowerCamelCase_       	=		MultilingualCLIP(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		text_encoder.eval()

      return text_encoder






    @property
    def   UpperCamelCase			(			self	:				int   )	->						List[Any]:
      torch.manual_seed(0   )

      lowerCamelCase_       	=		{
          'in_channels': 4,
          # Out channels is double in channels because predicts mean and variance
          'out_channels': 8,
          'addition_embed_type': 'text_image',
          'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
          'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
          'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
          'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
          'layers_per_block': 1,
          'encoder_hid_dim': self.text_embedder_hidden_size,
          'encoder_hid_dim_type': 'text_image_proj',
          'cross_attention_dim': self.cross_attention_dim,
          'attention_head_dim': 4,
          'resnet_time_scale_shift': 'scale_shift',
          'class_embed_type': None,
      }

      lowerCamelCase_       	=		UNetaDConditionModel(**__SCREAMING_SNAKE_CASE   )
      return model






    @property
    def   UpperCamelCase			(			self	:				Tuple   )	->						Optional[Any]:
      return {
          "block_out_channels": [32, 64],
          "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 12,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }






    @property
    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Optional[Any]:
      torch.manual_seed(0   )
      lowerCamelCase_       	=		VQModel(**self.dummy_movq_kwargs   )
      return model






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						List[Any]:
      lowerCamelCase_       	=		self.dummy_text_encoder
      lowerCamelCase_       	=		self.dummy_tokenizer
      lowerCamelCase_       	=		self.dummy_unet
      lowerCamelCase_       	=		self.dummy_movq

      lowerCamelCase_       	=		{
          'num_train_timesteps': 1000,
          'beta_schedule': 'linear',
          'beta_start': 0.00_085,
          'beta_end': 0.012,
          'clip_sample': False,
          'set_alpha_to_one': False,
          'steps_offset': 0,
          'prediction_type': 'epsilon',
          'thresholding': False,
      }

      lowerCamelCase_       	=		DDIMScheduler(**__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		{
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }

      return components






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				Dict=0   )	->						str:
      lowerCamelCase_       	=		floats_tensor((1, self.cross_attention_dim)			,		rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		floats_tensor((1, self.cross_attention_dim)			,		rng=random.Random(seed + 1   )   ).to(__SCREAMING_SNAKE_CASE   )
      # create init_image
      lowerCamelCase_       	=		floats_tensor((1, 3, 64, 64)			,		rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		image.cpu().permute(0			,		2			,		3			,		1   )[0]
      lowerCamelCase_       	=		Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE   )   ).convert('RGB'   ).resize((256, 256)   )

      if str(__SCREAMING_SNAKE_CASE   ).startswith('mps'   ):
        lowerCamelCase_       	=		torch.manual_seed(__SCREAMING_SNAKE_CASE   )
      else:
        lowerCamelCase_       	=		torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		{
          'prompt': 'horse',
          'image': init_image,
          'image_embeds': image_embeds,
          'negative_image_embeds': negative_image_embeds,
          'generator': generator,
          'height': 64,
          'width': 64,
          'num_inference_steps': 10,
          'guidance_scale': 7.0,
          'strength': 0.2,
          'output_type': 'np',
      }
      return inputs






    def   UpperCamelCase			(			self	:				Tuple   )	->						Any:
      lowerCamelCase_       	=		'cpu'

      lowerCamelCase_       	=		self.get_dummy_components()

      lowerCamelCase_       	=		self.pipeline_class(**__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		pipe.to(__SCREAMING_SNAKE_CASE   )

      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )   )
      lowerCamelCase_       	=		output.images

      lowerCamelCase_       	=		pipe(
          **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )			,		return_dict=__SCREAMING_SNAKE_CASE			,		)[0]

      lowerCamelCase_       	=		image[0, -3:, -3:, -1]
      lowerCamelCase_       	=		image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)

      lowerCamelCase_       	=		np.array(
          [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]   )
      assert (
          np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
      ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
      ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






@slow
@require_torch_gpu
class 	a		(   unittest.TestCase						):






    def   UpperCamelCase			(			self	:				Tuple   )	->						List[str]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






    def   UpperCamelCase			(			self	:				Any   )	->						int:
      lowerCamelCase_       	=		load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinsky/kandinsky_img2img_frog.npy'   )

      lowerCamelCase_       	=		load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'   )
      lowerCamelCase_       	=		'A red cartoon frog, 4k'

      lowerCamelCase_       	=		KandinskyPriorPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1-prior'			,		torch_dtype=torch.floataa   )
      pipe_prior.to(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		KandinskyImgaImgPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1'			,		torch_dtype=torch.floataa   )
      lowerCamelCase_       	=		pipeline.to(__SCREAMING_SNAKE_CASE   )

      pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		torch.Generator(device='cpu'   ).manual_seed(0   )
      lowerCamelCase_	,      lowerCamelCase_       	=		pipe_prior(
          __SCREAMING_SNAKE_CASE			,		generator=__SCREAMING_SNAKE_CASE			,		num_inference_steps=5			,		negative_prompt=''			,		).to_tuple()

      lowerCamelCase_       	=		pipeline(
          __SCREAMING_SNAKE_CASE			,		image=__SCREAMING_SNAKE_CASE			,		image_embeds=__SCREAMING_SNAKE_CASE			,		negative_image_embeds=__SCREAMING_SNAKE_CASE			,		generator=__SCREAMING_SNAKE_CASE			,		num_inference_steps=100			,		height=768			,		width=768			,		strength=0.2			,		output_type='np'			,		)

      lowerCamelCase_       	=		output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )




